Abstract

Digital tomosynthesis (DTS) with a linear accelerator-mounted imaging system provides a means of reconstructing tomographic images from radiographic projections over a limited gantry arc, thus requiring only a few seconds to acquire. Its application in the thorax, however, often results in blurred images from respiration-induced motion. This work evaluates the feasibility of respiration-correlated (RC) DTS for soft-tissue visualization and patient positioning. Image data acquired with a gantry-mounted kilovoltage imaging system while recording respiration were retrospectively analyzed from patients receiving radiotherapy for non-small-cell lungcarcinoma. Projection images spanning an approximately 30° gantry arc were sorted into four respiration phase bins prior to DTS reconstruction, which uses a backprojection, followed by a procedure to suppress structures above and below the reconstruction plane of interest. The DTS images were reconstructed in planes at different depths through the patient and normal to a user-selected angle close to the center of the arc. The localization accuracy of RC-DTS was assessed via a comparison with CBCT. Evaluation of RC-DTS in eight tumors shows visible reduction in image blur caused by the respiratory motion. It also allows the visualization of tumor motion extent. The best image quality is achieved at the end-exhalation phase of the respiratory motion. Comparison of RC-DTS with respiration-correlated cone-beam CT in determining tumor position, motion extent and displacement between treatment sessions shows agreement in most cases within 2–3 mm, comparable in magnitude to the intraobserver repeatability of the measurement. These results suggest the method’s applicability for soft-tissue image guidance in lung, but must be confirmed with further studies in larger numbers of patients.

Received 30 December 2008Revised 11 December 2009Accepted 19 January 2010Published online 23 February 2010

Acknowledgments:

This work was supported in part by Award Nos. P01CA59017, T32CA61801, and R01CA126993 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The authors also thank Hai Pham for coding some of the analysis software and Joseph McNamara for analyzing of some of the data presented in this paper.